



'''simple docstring'''



from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class 						lowerCamelCase_				(       snake_case_      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													return [
													    {"col_1": 3, "col_2": "a"},
													    {"col_1": 2, "col_2": "b"},
													    {"col_1": 1, "col_2": "c"},
													    {"col_1": 0, "col_2": "d"},
													]







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      {'''col_1''': [3, 2, 1, 0], '''col_2''': ['''a''', '''b''', '''c''', '''d''']}
													return Dataset.from_dict(lowerCAmelCase__					)







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self._create_example_records()
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      Dataset.from_list(lowerCAmelCase__					)
													self.assertListEqual(dset.column_names					,     ['''col_1''', '''col_2''']					)
													for i, r in enumerate(lowerCAmelCase__					):
																				self.assertDictEqual(lowerCAmelCase__					,     example_records[i]					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self._create_example_records()
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      Dataset.from_list(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}					)
													self.assertEqual(dset.info					,     dset_from_dict.info					)







						def        __lowercase     (	self :   Optional[Any]					):  # checks what happens with missing columns

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      [{'''col_1''': 1}, {'''col_2''': '''x'''}]
													SCREAMING_SNAKE_CASE       :   str         =      Dataset.from_list(lowerCAmelCase__					)
													self.assertDictEqual(dset[0]					,     {'''col_1''': 1}					)
													self.assertDictEqual(dset[1]					,     {'''col_1''': None}					)  # NB: first record is used for columns







						def        __lowercase     (	self :   str					):  # checks if the type can be inferred from the second record

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      [{'''col_1''': []}, {'''col_1''': [1, 2]}]
													SCREAMING_SNAKE_CASE       :   List[str]         =      Dataset.from_list(lowerCAmelCase__					)
													self.assertEqual(dset.info.features['''col_1''']					,     Sequence(Value('''int64'''					)					)					)







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      Dataset.from_list([]					)
													self.assertEqual(len(lowerCAmelCase__					)					,     0					)
													self.assertListEqual(dset.column_names					,     []					)









'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					Optional[int]       ,		A	:					int					):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE       :   str         =      TaConfig.from_json_file(A					)
							print(F"""Building PyTorch model from configuration: {config}"""					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TaForConditionalGeneration(A					)

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(A       ,		A       ,		A					)

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Union[str, Any]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_  :							Tuple             =    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




